Trying to estimate the probability of a flashover occurring during a compartment fire is a complex problem as flashovers depend on a large number of factors (for example, room size, air flow etc.). Artificial neural networks appear well suited to problems of this nature as they can be trained to understand the explicit and inexplicit factors that might cause flashover. For this reason, artificial neural networks were investigated as a potential tool for predicting flashovers in a room with known, or estimable, compartment characteristics. In order to deal with uncertainties that can exist in a model's resutls, a statistical analysis was employed to identify confidence intervals for predicted flashover prbabilities. In addition, Monte Carlo simulation of trained artificial neural networks was also employed to deal with uncertainties in initial room characteristic estimates. This paper discusses these analyses and comments on the results that were obtained when artificial neural networks were developed, trained and tested on the data supplied.
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